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A Novel Data Embedding Method Using Adaptive Pixel Pair Matching
ABSTRACT:
This
paper proposes a new data-hiding method based on pixel pair matching
(PPM). The basic idea of PPM is to use the values of pixel pair as a
reference coordinate, and search a coordinate in the neighborhood set of
this pixel pair according to a given message digit. The pixel pair is
then replaced by the searched coordinate to conceal the digit.
Exploiting modification direction (EMD) and diamond encoding (DE) are
two data-hiding methods proposed recently based on PPM. The maximum
capacity of EMD is 1.161 bpp and DE extends the payload of EMD by
embedding digits in a larger notational system. The proposed method
offers lower distortion than DE by providing more compact neighborhood
sets and allowing embedded digits in any notational system. Compared
with the optimal pixel adjustment process (OPAP) method, the proposed
method always has lower distortion for various payloads. Experimental
results reveal that the proposed method not only provides better
performance than those of OPAP and DE, but also is secure under the
detection of some well-known steganalysis techniques.
SYSTEM ACHITECTURE:
EXISTING SYSTEM:
The
least significant bit substitution method, referred to as LSB in this
paper, is a well-known data-hiding method. This method is easy to
implement with low CPU cost, and has become one of the popular embedding
techniques. However, in LSB embedding, the pixels with even values will
be increased by one or kept unmodified. The pixels with odd values will
be decreased by one or kept unmodified. Therefore, the imbalanced
embedding distortion emerges and is vulnerable to steganalysis.
Optimal
pixel adjustment process (OPAP) method to reduce the distortion caused
by LSB replacement. In their method, if message bits are embedded into
the right-most LSBs of an -bit pixel, other bits are adjusted by a
simple evaluation. Namely, if the adjusted result offers a smaller
distortion, these bits are either replaced by the adjusted result or
otherwise kept unmodified.
Exploiting modification direction (EMD) and diamond encoding (DE) are two data-hiding methods proposed recently based on PPM
DISADVANTAGES OF EXISTING SYSTEM:
· Imbalanced embedding distortion emerges and is vulnerable to steganalysis.
· The existing technique can be easily cracked.
PROPOSED SYSTEM:
The
basic idea of PPM is to use the values of pixel pair as a reference
coordinate, and search a coordinate in the neighborhood set of this
pixel pair according to a given message digit. The pixel pair is then
replaced by the searched coordinate to conceal the digit.
This
paper proposes a new data embedding method to reduce the embedding
impact by providing a simple extraction function and a more compact
neighborhood set. The proposed method embeds more messages per
modification and thus increases the embedding efficiency. The image
quality obtained by the proposed method not only performs better than
those obtained by OPAP and DE, but also brings higher payload with less
detect ability. Moreover, the best notational system for data concealing
can be determined and employed in this new method according to the
given payload so that a lower image distortion can be achieved.
ADVANTAGES OF PROPOSED SYSTEM:
The
proposed method offers lower distortion than DE by providing more
compact neighborhood sets and allowing embedded digits in any notational
system. Compared with the optimal pixel adjustment process (OPAP)
method, the proposed method always has lower distortion for various
payloads. Experimental results reveal that the proposed method not only
provides better performance than those of OPAP and DE, but also is
secure under the detection of some well-known steganalysis techniques.
MODULES:
· Extraction Function and Neighborhood Set
· Embedding Procedure
· Extraction Procedure
· Statistical Analysis of the Histogram Differences
MODULES DESCRIPTION:
Extraction Function and Neighborhood Set
In
this module we perform the action of extraction function and
neighborhood set. Where the system does a new data embedding method to
reduce the embedding impact by providing a simple extraction function
and a more compact neighborhood set. The proposed method embeds more
messages per modification and thus increases the embedding efficiency.
The image quality obtained by the proposed method not only performs
better than those obtained by OPAP and DE, but also brings higher
payload with less detectability. Moreover, the best notational system
for data concealing can be determined and employed in this new method
according to the given payload so that a lower image distortion can be
achieved.
Embedding Procedure
Input: Cover image of size , secret bit stream, and key .
Output: Stego image , , , and .
1. Find the minimum satisfying, and convert into a list of digits with a -ary notational system.
2. Solve the discrete optimization problem to find and.
3. In the region defined by, record the coordinate such that , .
4. Construct a no repeat random embedding sequence using a key .
5.
To embed a message digit, two pixels in the cover image are selected
according to the embedding sequence, and calculate the modulus distance
between and , then replace with .
6. Repeat Step 5 until all the message digits are embedded.
Extraction Procedure
To
extract the embedded message digits, pixel pairs are scanned in the
same order as in the embedding procedure. The embedded message digits
are the values of extraction function of the scanned pixel pairs.
Input: Stego image, , , and .
Output: Secret bit stream.
1. Construct the embedding sequence using the key.
2. Select two pixels according to the embedding sequence.
3. Calculate, the result is the embedded digit.
4. Repeat Steps 2 and 3 until all the message digits are extracted.
5. Finally, the message bits can be obtained by converting the extracted message digits into a binary bit stream.
Statistical Analysis of the Histogram Differences
In
this module, we perform the goal of system analysis by using histogram
technique. The goal of steganography is to evade statistical detection.
It is apparent that MSE is not a good measure of security against the
detection of steganalysis. Histograms are used to plot density of data,
and often for density estimation: estimating the probability density
function of the underlying variable. The total area of a histogram used
for probability density is always normalized to 1. If the lengths of the
intervals on the x-axis are all 1, then a histogram is identical to a
relative frequency plot.
HARDWARE REQUIREMENTS
• SYSTEM : Pentium IV 2.4 GHz
• HARD DISK : 40 GB
• MONITOR : 15 VGA colour
• MOUSE : Logitech.
• RAM : 256 MB
• KEYBOARD : 110 keys enhanced.
SOFTWARE REQUIREMENTS
• Operating system : Windows XP Professional
• Front End : JAVA
• Tool : NETBEANS IDE
REFERENCE:
Wien Hong and Tung-Shou Chen, “A Novel Data Embedding Method Using Adaptive Pixel Pair Matching” , IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 7, NO. 1, FEBRUARY 2012.
